{
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      "name": "python3"
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    "language_info": {
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
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      "version": "3.7.6"
    },
    "colab": {
      "name": "2LunarLander_dqn.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBBH0k8rc5iW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import typing\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "\n",
        "QNetwork = nn.Module\n",
        "\n",
        "\n",
        "class LambdaLayer(nn.Module):\n",
        "    \n",
        "    def __init__(self, f):\n",
        "        super().__init__()\n",
        "        self._f = f\n",
        "        \n",
        "    def forward(self, X):\n",
        "        return self._f(X)\n",
        "\n",
        "\n",
        "def make_deep_q_network_fn(action_size: int) -> typing.Callable[[], QNetwork]:\n",
        "    \n",
        "    def deep_q_network_fn() -> QNetwork:\n",
        "        q_network = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=4, out_channels=32, kernel_size=8, stride=4),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=2, stride=1),\n",
        "            nn.ReLU(),\n",
        "            LambdaLayer(lambda tensor: tensor.view(tensor.size(0), -1)),\n",
        "            nn.Linear(in_features=25024, out_features=512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(in_features=512, out_features=action_size)\n",
        "        )\n",
        "        return q_network\n",
        "    \n",
        "    return deep_q_network_fn"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e9fDs-Vwc5ii",
        "colab_type": "text"
      },
      "source": [
        "### The Loss Function\n",
        "\n",
        "The $Q$-learning update at iteration $i$ uses the following loss function\n",
        "\n",
        "$$ \\mathcal{L_i}(\\theta_i) = \\mathbb{E}_{(s, a, r, s') \\sim U(D)} \\Bigg[\\bigg(r + \\gamma \\max_{a'} Q\\big(s', a'; \\theta_i^{-}\\big) - Q\\big(s, a; \\theta_i\\big)\\bigg)^2\\Bigg] $$\n",
        "\n",
        "where $\\gamma$ is the discount factor determining the agent’s horizon, $\\theta_i$ are the parameters of the $Q$-network at iteration $i$ and $\\theta_i^{-}$ are the $Q$-network parameters used to compute the target at iteration $i$. The target network parameters $\\theta_i^{-}$ are only updated with the $Q$-network parameters $\\theta_i$ every $C$ steps and are held fixed between individual updates. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCWYziRYc5ij",
        "colab_type": "text"
      },
      "source": [
        "### Experience Replay\n",
        "\n",
        "To perform *experience replay* the authors store the agent's experiences $e_t$ as represented by the tuple\n",
        "\n",
        "$$ e_t = (s_t, a_t, r_t, s_{t+1}) $$\n",
        "\n",
        "consisting of the observed state in period $t$, the reward received in period $t$, the action taken in period $t$, and the resulting state in period $t+1$. The dataset of agent experiences at period $t$ consists of the set of past experiences.\n",
        "\n",
        "$$ D_t = \\{e1, e2, ..., e_t \\} $$\n",
        "\n",
        "Depending on the task it may note be feasible for the agent to store the entire history of past experiences.\n",
        "\n",
        "During learning Q-learning updates are computed based on samples (or minibatches) of experience $(s,a,r,s')$, drawn uniformly at random from the pool of stored samples $D_t$.\n",
        "\n",
        "The following is my Python implmentation of these ideas. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Of1-f8Adc5il",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import collections\n",
        "import typing\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "_field_names = [\n",
        "    \"state\",\n",
        "    \"action\",\n",
        "    \"reward\",\n",
        "    \"next_state\",\n",
        "    \"done\"\n",
        "]\n",
        "Experience = collections.namedtuple(\"Experience\", field_names=_field_names)\n",
        "\n",
        "\n",
        "class ExperienceReplayBuffer:\n",
        "    \"\"\"Fixed-size buffer to store experience tuples.\"\"\"\n",
        "\n",
        "    def __init__(self,\n",
        "                 batch_size: int,\n",
        "                 buffer_size: int = None,\n",
        "                 random_state: np.random.RandomState = None) -> None:\n",
        "        \"\"\"\n",
        "        Initialize an ExperienceReplayBuffer object.\n",
        "\n",
        "        Parameters:\n",
        "        -----------\n",
        "        buffer_size (int): maximum size of buffer\n",
        "        batch_size (int): size of each training batch\n",
        "        seed (int): random seed\n",
        "        \n",
        "        \"\"\"\n",
        "        self._batch_size = batch_size\n",
        "        self._buffer_size = buffer_size\n",
        "        self._buffer = collections.deque(maxlen=buffer_size)\n",
        "        self._random_state = np.random.RandomState() if random_state is None else random_state\n",
        "        \n",
        "    def __len__(self) -> int:\n",
        "        return len(self._buffer)\n",
        "    \n",
        "    @property\n",
        "    def batch_size(self) -> int:\n",
        "        return self._batch_size\n",
        "    \n",
        "    @property\n",
        "    def buffer_size(self) -> int:\n",
        "        return self._buffer_size\n",
        "\n",
        "    def is_full(self) -> bool:\n",
        "        return len(self._buffer) == self._buffer_size\n",
        "    \n",
        "    def append(self, experience: Experience) -> None:\n",
        "        \"\"\"Add a new experience to memory.\"\"\"\n",
        "        self._buffer.append(experience)\n",
        "    \n",
        "    def sample(self) -> typing.List[Experience]:\n",
        "        \"\"\"Randomly sample a batch of experiences from memory.\"\"\"\n",
        "        idxs = self._random_state.randint(len(self._buffer), size=self._batch_size)\n",
        "        experiences = [self._buffer[idx] for idx in idxs]\n",
        "        return experiences\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v5iAtJXjc5i6",
        "colab_type": "text"
      },
      "source": [
        "### The Deep Q-Network Algorithm\n",
        "\n",
        "The following is Python pseudo-code for the Deep Q-Network (DQN) algorithm. For more fine-grained details of the DQN algorithm see the methods section of [Minh et al 2015](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf).\n",
        "\n",
        "```python\n",
        "\n",
        "# hyper-parameters\n",
        "batch_size = 32 # number of experience tuples used in computing the gradient descent parameter update.\n",
        "buffer_size = 10000 # number of experience tuples stored in the replay buffer\n",
        "gamma = 0.99 # discount factor used in the Q-learning update\n",
        "target_network_update_frequency = 4 # frequency (measured in parameter updates) with which target network is updated.\n",
        "update_frequency = 4 # frequency (measured in number of timesteps) with which q-network parameters are updated.\n",
        "\n",
        "# initilizing the various data structures\n",
        "replay_buffer = ExperienceReplayBuffer(batch_size, buffer_size, seed)\n",
        "local_q_network = initialize_q_network()\n",
        "target_q_network = initialize_q_network()\n",
        "synchronize_q_networks(target_q_network, local_q_network)\n",
        "\n",
        "for i in range(number_episodes)\n",
        "\n",
        "    # initialize the environment state\n",
        "    state = env.reset()\n",
        "\n",
        "    # simulate a single training episode\n",
        "    done = False\n",
        "    timesteps = 0\n",
        "    parameter_updates = 0\n",
        "    while not done:\n",
        "\n",
        "        # greedy action based on Q(s, a; theta)\n",
        "        action = agent.choose_epsilon_greedy_action(state) \n",
        "\n",
        "        # update the environment based on the chosen action\n",
        "        next_state, reward, done = env.step(action)\n",
        "\n",
        "        # agent records experience in its replay buffer\n",
        "        experience = (state, action, reward, next_state, done)\n",
        "        agent.replay_buffer.append(experience)\n",
        "\n",
        "        # agent samples a mini-batch of experiences from its replay buffer\n",
        "        experiences = agent.replay_buffer.sample()\n",
        "        states, actions, rewards, next_states, dones = experiences\n",
        "\n",
        "        # agent learns every update_frequency timesteps\n",
        "        if timesteps % update_frequency == 0:\n",
        "            \n",
        "            # compute the Q^- values using the Q-learning formula\n",
        "            target_q_values = q_learning_update(target_q_network, rewards, next_states, dones)\n",
        "\n",
        "            # compute the Q values\n",
        "            local_q_values = local_q_network(states, actions)\n",
        "\n",
        "            # agent updates the parameters theta using gradient descent\n",
        "            loss = mean_squared_error(target_q_values, local_q_values)\n",
        "            gradient_descent_update(loss)\n",
        "            \n",
        "            parameter_updates += 1\n",
        "\n",
        "        # every target_network_update_frequency timesteps set theta^- = theta\n",
        "        if parameter_updates % target_network_update_frequency == 0:\n",
        "            synchronize_q_networks(target_q_network, local_q_network)\n",
        "```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flTWLf73c5i9",
        "colab_type": "text"
      },
      "source": [
        "## Solving the `LunarLander-v2` environment\n",
        "\n",
        "In the rest of this blog post I will use the DQN algorithm to train an agent to solve the [LunarLander-v2](https://gym.openai.com/envs/LunarLander-v2/) environment from [OpenAI](https://openai.com/).\n",
        "\n",
        "In this environment the landing pad is always at coordinates (0,0). The reward for moving the lander from the top of the screen to landing pad and arriving at zero speed is typically between 100 and 140 points. Firing the main engine is -0.3 points each frame (so the lander is incentivized to fire the engine as few times possible). If the lander moves away from landing pad it loses reward (so the lander is incentived to land in the designated landing area). The lander is also incentived to land \"gracefully\" (and not crash in the landing area!).\n",
        "\n",
        "A training episode finishes if the lander crashes (-100 points) or comes to rest (+100 points). Each leg with ground contact receives and additional +10 points. The task is considered \"solved\" if the lander is able to achieve 200 points (I will actually be more stringent and define \"solved\" as achieving over 200 points on average in the most recent 100 training episodes).\n",
        "\n",
        "### Action Space \n",
        "\n",
        "There are four discrete actions available: \n",
        "\n",
        "0. Do nothing.\n",
        "1. Fire the left orientation engine.\n",
        "2. Fire main engine.\n",
        "3. Fire the right orientation engine."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1BmMaLINc5i_",
        "colab_type": "text"
      },
      "source": [
        "### Google Colab Preamble\n",
        "\n",
        "If you are playing around with this notebook on Google Colab, then you will need to run the following cell in order to install the required OpenAI dependencies into the environment."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wvzm9ShTc5jB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "3b30d437-58f9-46e7-9e71-bc40c4546f2f"
      },
      "source": [
        "%%bash\n",
        "\n",
        "# install required system dependencies\n",
        "apt-get install -y xvfb x11-utils\n",
        "\n",
        "# install required python dependencies (might need to install additional gym extras depending)\n",
        "pip install gym[box2d]==0.17.* pyvirtualdisplay==0.2.* PyOpenGL==3.1.* PyOpenGL-accelerate==3.1.*\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists...\n",
            "Building dependency tree...\n",
            "Reading state information...\n",
            "x11-utils is already the newest version (7.7+3build1).\n",
            "xvfb is already the newest version (2:1.19.6-1ubuntu4.4).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-440\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 35 not upgraded.\n",
            "Requirement already satisfied: gym[box2d]==0.17.* in /usr/local/lib/python3.6/dist-packages (0.17.2)\n",
            "Requirement already satisfied: pyvirtualdisplay==0.2.* in /usr/local/lib/python3.6/dist-packages (0.2.5)\n",
            "Requirement already satisfied: PyOpenGL==3.1.* in /usr/local/lib/python3.6/dist-packages (3.1.5)\n",
            "Requirement already satisfied: PyOpenGL-accelerate==3.1.* in /usr/local/lib/python3.6/dist-packages (3.1.5)\n",
            "Requirement already satisfied: cloudpickle<1.4.0,>=1.2.0 in /usr/local/lib/python3.6/dist-packages (from gym[box2d]==0.17.*) (1.3.0)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.6/dist-packages (from gym[box2d]==0.17.*) (1.18.5)\n",
            "Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /usr/local/lib/python3.6/dist-packages (from gym[box2d]==0.17.*) (1.5.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from gym[box2d]==0.17.*) (1.4.1)\n",
            "Requirement already satisfied: box2d-py~=2.3.5; extra == \"box2d\" in /usr/local/lib/python3.6/dist-packages (from gym[box2d]==0.17.*) (2.3.8)\n",
            "Requirement already satisfied: EasyProcess in /usr/local/lib/python3.6/dist-packages (from pyvirtualdisplay==0.2.*) (0.3)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyglet<=1.5.0,>=1.4.0->gym[box2d]==0.17.*) (0.16.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NAlQ8Me7c5jH",
        "colab_type": "text"
      },
      "source": [
        "The code in the cell below creates a virtual display in the background that your Gym Envs can connect to for rendering. You can adjust the size of the virtual buffer as you like but you must set `visible=False`.\n",
        "\n",
        "**This code only needs to be run once per session to start the display.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXeHSrZFc5jI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pyvirtualdisplay\n",
        "\n",
        "\n",
        "_display = pyvirtualdisplay.Display(visible=False,  # use False with Xvfb\n",
        "                                    size=(1400, 900))\n",
        "_ = _display.start()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tynfeGv_c5jR",
        "colab_type": "text"
      },
      "source": [
        "### Binder Preamble\n",
        "\n",
        "If you are running this code on Binder, then there isn't really much to do as all the software is pre-installed. However you do still need to run the code in the cell below to creates a virtual display in the background that your Gym Envs can connect to for rendering. You can adjust the size of the virtual buffer as you like but you must set `visible=False`.\n",
        "\n",
        "*This code only needs to be run once per session to start the display.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EntU0rXcc5jT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pyvirtualdisplay\n",
        "\n",
        "\n",
        "_display = pyvirtualdisplay.Display(visible=False,  # use False with Xvfb\n",
        "                                    size=(1400, 900))\n",
        "_ = _display.start()"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NXANasAc5jd",
        "colab_type": "text"
      },
      "source": [
        "### Creating the Gym environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ssQ3ed61c5jg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import gym\n",
        "\n",
        "env = gym.make('LunarLander-v2')\n",
        "_ = env.seed(42)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCbiT6-tc5jo",
        "colab_type": "text"
      },
      "source": [
        "### Defining a generic `Agent` and `train` loop\n",
        "\n",
        "In the cell below I define a fairly generic training loop for training and `Agent` to solve a task in a given `gym.Env` environment. In working through the hands-on portions of the [Udacity](https://www.udacity.com/) [*Deep Reinforcement Learning Nanodegree*](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) I found myself writing similar code over and over again to train the agent to solve a task. This is my first attempt to write something that I might be able to reuse on the course going forward."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2GqXNZCZc5jp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Agent:\n",
        "    \n",
        "    def choose_action(self, state: np.array) -> int:\n",
        "        \"\"\"Rule for choosing an action given the current state of the environment.\"\"\"\n",
        "        raise NotImplementedError\n",
        "        \n",
        "    def save(self, filepath) -> None:\n",
        "        \"\"\"Save any important agent state to a file.\"\"\"\n",
        "        raise NotImplementedError\n",
        "        \n",
        "    def step(self,\n",
        "             state: np.array,\n",
        "             action: int,\n",
        "             reward: float,\n",
        "             next_state: np.array,\n",
        "             done: bool) -> None:\n",
        "        \"\"\"Update agent's state after observing the effect of its action on the environment.\"\"\"\n",
        "        raise NotImplmentedError\n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vATxdcQc5jx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def _train_for_at_most(agent: Agent, env: gym.Env, max_timesteps: int) -> int:\n",
        "    \"\"\"Train agent for a maximum number of timesteps.\"\"\"\n",
        "    state = env.reset()\n",
        "    score = 0\n",
        "    for t in range(max_timesteps):\n",
        "        action = agent.choose_action(state)\n",
        "        next_state, reward, done, _ = env.step(action)\n",
        "        agent.step(state, action, reward, next_state, done)\n",
        "        state = next_state\n",
        "        score += reward\n",
        "        if done:\n",
        "            break\n",
        "    return score\n",
        "\n",
        "                \n",
        "def _train_until_done(agent: Agent, env: gym.Env) -> float:\n",
        "    \"\"\"Train the agent until the current episode is complete.\"\"\"\n",
        "    state = env.reset()\n",
        "    score = 0\n",
        "    done = False\n",
        "    while not done:\n",
        "        action = agent.choose_action(state)\n",
        "        next_state, reward, done, _ = env.step(action)\n",
        "        agent.step(state, action, reward, next_state, done)\n",
        "        state = next_state\n",
        "        score += reward\n",
        "    return score\n",
        "\n",
        "\n",
        "def train(agent: Agent,\n",
        "          env: gym.Env,\n",
        "          checkpoint_filepath: str,\n",
        "          target_score: float,\n",
        "          number_episodes: int,\n",
        "          maximum_timesteps=None) -> typing.List[float]:\n",
        "    \"\"\"\n",
        "    Reinforcement learning training loop.\n",
        "    \n",
        "    Parameters:\n",
        "    -----------\n",
        "    agent (Agent): an agent to train.\n",
        "    env (gym.Env): an environment in which to train the agent.\n",
        "    checkpoint_filepath (str): filepath used to save the state of the trained agent.\n",
        "    number_episodes (int): maximum number of training episodes.\n",
        "    maximum_timsteps (int): maximum number of timesteps per episode.\n",
        "    \n",
        "    Returns:\n",
        "    --------\n",
        "    scores (list): collection of episode scores from training.\n",
        "    \n",
        "    \"\"\"\n",
        "    scores = []\n",
        "    most_recent_scores = collections.deque(maxlen=100)\n",
        "    for i in range(number_episodes):\n",
        "        if maximum_timesteps is None:\n",
        "            score = _train_until_done(agent, env)\n",
        "        else:\n",
        "            score = _train_for_at_most(agent, env, maximum_timesteps)         \n",
        "        scores.append(score)\n",
        "        most_recent_scores.append(score)\n",
        "        \n",
        "        average_score = sum(most_recent_scores) / len(most_recent_scores)\n",
        "        if average_score >= target_score:\n",
        "            print(f\"\\nEnvironment solved in {i:d} episodes!\\tAverage Score: {average_score:.2f}\")\n",
        "            agent.save(checkpoint_filepath)\n",
        "            break\n",
        "        if (i + 1) % 100 == 0:\n",
        "            print(f\"\\rEpisode {i + 1}\\tAverage Score: {average_score:.2f}\")\n",
        "\n",
        "    return scores"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CHHq9c7c5j3",
        "colab_type": "text"
      },
      "source": [
        "### Creating a `DeepQAgent`\n",
        "\n",
        "The code in the cell below encapsulates much of the logic of the DQN algorithm in a `DeepQAgent` class. Since the `LunarLander-v2` task is not well suited for convolutional neural networks, the agent uses a simple three layer dense neural network with ReLU activation functions to approximate the action-value function $Q$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxtRgm9Bc5j5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch import optim\n",
        "from torch.nn import functional as F\n",
        "\n",
        "\n",
        "class DeepQAgent(Agent):\n",
        "\n",
        "    def __init__(self,\n",
        "                 state_size: int,\n",
        "                 action_size: int,\n",
        "                 number_hidden_units: int,\n",
        "                 optimizer_fn: typing.Callable[[typing.Iterable[torch.nn.Parameter]], optim.Optimizer],\n",
        "                 batch_size: int,\n",
        "                 buffer_size: int,\n",
        "                 epsilon_decay_schedule: typing.Callable[[int], float],\n",
        "                 alpha: float,\n",
        "                 gamma: float,\n",
        "                 update_frequency: int,\n",
        "                 seed: int = None) -> None:\n",
        "        \"\"\"\n",
        "        Initialize a DeepQAgent.\n",
        "        \n",
        "        Parameters:\n",
        "        -----------\n",
        "        state_size (int): the size of the state space.\n",
        "        action_size (int): the size of the action space.\n",
        "        number_hidden_units (int): number of units in the hidden layers.\n",
        "        optimizer_fn (callable): function that takes Q-network parameters and returns an optimizer.\n",
        "        batch_size (int): number of experience tuples in each mini-batch.\n",
        "        buffer_size (int): maximum number of experience tuples stored in the replay buffer.\n",
        "        epsilon_decay_schdule (callable): function that takes episode number and returns epsilon.\n",
        "        alpha (float): rate at which the target q-network parameters are updated.\n",
        "        gamma (float): Controls how much that agent discounts future rewards (0 < gamma <= 1).\n",
        "        update_frequency (int): frequency (measured in time steps) with which q-network parameters are updated.\n",
        "        seed (int): random seed\n",
        "        \n",
        "        \"\"\"\n",
        "        self._state_size = state_size\n",
        "        self._action_size = action_size\n",
        "        self._device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "        \n",
        "        # set seeds for reproducibility\n",
        "        self._random_state = np.random.RandomState() if seed is None else np.random.RandomState(seed)\n",
        "        if seed is not None:\n",
        "            torch.manual_seed(seed)\n",
        "        if torch.cuda.is_available():\n",
        "            torch.backends.cudnn.deterministic = True\n",
        "            torch.backends.cudnn.benchmark = False\n",
        "        \n",
        "        # initialize agent hyperparameters\n",
        "        self._experience_replay_buffer = ExperienceReplayBuffer(batch_size, buffer_size, seed)\n",
        "        self._epsilon_decay_schedule = epsilon_decay_schedule\n",
        "        self._alpha = alpha\n",
        "        self._gamma = gamma\n",
        "        \n",
        "        # initialize Q-Networks\n",
        "        self._update_frequency = update_frequency\n",
        "        self._local_q_network = self._initialize_q_network(number_hidden_units)\n",
        "        self._target_q_network = self._initialize_q_network(number_hidden_units)\n",
        "        self._synchronize_q_networks()\n",
        "        \n",
        "        # send the networks to the device\n",
        "        self._local_q_network.to(self._device)\n",
        "        self._target_q_network.to(self._device)\n",
        "        \n",
        "        # initialize the optimizer\n",
        "        self._optimizer = optimizer_fn(self._local_q_network.parameters())\n",
        "\n",
        "        # initialize some counters\n",
        "        self._number_episodes = 0\n",
        "        self._number_timesteps = 0\n",
        "        self._number_parameter_updates = 0\n",
        "        \n",
        "    def _initialize_q_network(self, number_hidden_units: int) -> nn.Module:\n",
        "        \"\"\"Create a neural network for approximating the action-value function.\"\"\"\n",
        "        q_network = nn.Sequential(\n",
        "            nn.Linear(in_features=self._state_size, out_features=number_hidden_units),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(in_features=number_hidden_units, out_features=number_hidden_units),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(in_features=number_hidden_units, out_features=self._action_size)\n",
        "        )\n",
        "        return q_network\n",
        "        \n",
        "    def _learn_from(self, experiences: typing.List[Experience]) -> None:\n",
        "        \"\"\"Heart of the Deep Q-learning algorithm.\"\"\"\n",
        "        states, actions, rewards, next_states, dones = (torch.Tensor(vs).to(self._device) for vs in zip(*experiences))\n",
        "        \n",
        "        # get max predicted Q values (for next states) from target model\n",
        "        next_target_q_values, _ = (self._target_q_network(next_states)\n",
        "                                       .detach()\n",
        "                                       .max(dim=1))\n",
        "        \n",
        "        # compute the new Q' values using the Q-learning formula\n",
        "        target_q_values = rewards + (self._gamma * next_target_q_values * (1 - dones))\n",
        "        \n",
        "        # get expected Q values from local model\n",
        "        _index = (actions.long()\n",
        "                         .unsqueeze(dim=1))\n",
        "        expected_q_values = (self._local_q_network(states)\n",
        "                                 .gather(dim=1, index=_index))\n",
        "        # compute the mean squared loss\n",
        "        loss = F.mse_loss(expected_q_values, target_q_values.unsqueeze(dim=1))\n",
        "        \n",
        "        # agent updates the parameters theta of Q using gradient descent\n",
        "        self._optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        self._optimizer.step()\n",
        "        \n",
        "        self._soft_update_target_q_network_parameters()\n",
        "                 \n",
        "    def _soft_update_target_q_network_parameters(self) -> None:\n",
        "        \"\"\"Soft-update of target q-network parameters with the local q-network parameters.\"\"\"\n",
        "        for target_param, local_param in zip(self._target_q_network.parameters(), self._local_q_network.parameters()):\n",
        "            target_param.data.copy_(self._alpha * local_param.data + (1 - self._alpha) * target_param.data)\n",
        "    \n",
        "    def _synchronize_q_networks(self) -> None:\n",
        "        \"\"\"Synchronize the target_q_network and the local_q_network.\"\"\"\n",
        "        _ = self._target_q_network.load_state_dict(self._local_q_network.state_dict())\n",
        "           \n",
        "    def _uniform_random_policy(self, state: torch.Tensor) -> int:\n",
        "        \"\"\"Choose an action uniformly at random.\"\"\"\n",
        "        return self._random_state.randint(self._action_size)\n",
        "        \n",
        "    def _greedy_policy(self, state: torch.Tensor) -> int:\n",
        "        \"\"\"Choose an action that maximizes the action_values given the current state.\"\"\"\n",
        "        # evaluate the network to compute the action values\n",
        "        self._local_q_network.eval()\n",
        "        with torch.no_grad():\n",
        "            action_values = self._local_q_network(state)\n",
        "        self._local_q_network.train()\n",
        "        \n",
        "        # choose the greedy action\n",
        "        action = (action_values.cpu()  # action_values might reside on the GPU!\n",
        "                               .argmax()\n",
        "                               .item())\n",
        "        return action\n",
        "    \n",
        "    def _epsilon_greedy_policy(self, state: torch.Tensor, epsilon: float) -> int:\n",
        "        \"\"\"With probability epsilon explore randomly; otherwise exploit knowledge optimally.\"\"\"\n",
        "        if self._random_state.random() < epsilon:\n",
        "            action = self._uniform_random_policy(state)\n",
        "        else:\n",
        "            action = self._greedy_policy(state)\n",
        "        return action\n",
        "\n",
        "    def choose_action(self, state: np.array) -> int:\n",
        "        \"\"\"\n",
        "        Return the action for given state as per current policy.\n",
        "        \n",
        "        Parameters:\n",
        "        -----------\n",
        "        state (np.array): current state of the environment.\n",
        "        \n",
        "        Return:\n",
        "        --------\n",
        "        action (int): an integer representing the chosen action.\n",
        "\n",
        "        \"\"\"\n",
        "        # need to reshape state array and convert to tensor\n",
        "        state_tensor = (torch.from_numpy(state)\n",
        "                             .unsqueeze(dim=0)\n",
        "                             .to(self._device))\n",
        "            \n",
        "        # choose uniform at random if agent has insufficient experience\n",
        "        if not self.has_sufficient_experience():\n",
        "            action = self._uniform_random_policy(state_tensor)\n",
        "        else:\n",
        "            epsilon = self._epsilon_decay_schedule(self._number_episodes)\n",
        "            action = self._epsilon_greedy_policy(state_tensor, epsilon)\n",
        "        return action\n",
        "    \n",
        "    def has_sufficient_experience(self) -> bool:\n",
        "        \"\"\"True if agent has enough experience to train on a batch of samples; False otherwise.\"\"\"\n",
        "        return len(self._experience_replay_buffer) >= self._experience_replay_buffer.batch_size\n",
        "    \n",
        "    def save(self, filepath: str) -> None:\n",
        "        \"\"\"\n",
        "        Saves the state of the DeepQAgent.\n",
        "        \n",
        "        Parameters:\n",
        "        -----------\n",
        "        filepath (str): filepath where the serialized state should be saved.\n",
        "        \n",
        "        Notes:\n",
        "        ------\n",
        "        The method uses `torch.save` to serialize the state of the q-network, \n",
        "        the optimizer, as well as the dictionary of agent hyperparameters.\n",
        "        \n",
        "        \"\"\"\n",
        "        checkpoint = {\n",
        "            \"q-network-state\": self._local_q_network.state_dict(),\n",
        "            \"optimizer-state\": self._optimizer.state_dict(),\n",
        "            \"agent-hyperparameters\": {\n",
        "                \"alpha\": self._alpha,\n",
        "                \"batch_size\": self._experience_replay_buffer.batch_size,\n",
        "                \"buffer_size\": self._experience_replay_buffer.buffer_size,\n",
        "                \"gamma\": self._gamma,\n",
        "                \"update_frequency\": self._update_frequency\n",
        "            }\n",
        "        }\n",
        "        torch.save(checkpoint, filepath)\n",
        "        \n",
        "    def step(self, state: np.array, action: int, reward: float, next_state: np.array, done: bool) -> None:\n",
        "        \"\"\"\n",
        "        Updates the agent's state based on feedback received from the environment.\n",
        "        \n",
        "        Parameters:\n",
        "        -----------\n",
        "        state (np.array): the previous state of the environment.\n",
        "        action (int): the action taken by the agent in the previous state.\n",
        "        reward (float): the reward received from the environment.\n",
        "        next_state (np.array): the resulting state of the environment following the action.\n",
        "        done (bool): True is the training episode is finised; false otherwise.\n",
        "        \n",
        "        \"\"\"\n",
        "        # save experience in the experience replay buffer\n",
        "        experience = Experience(state, action, reward, next_state, done)\n",
        "        self._experience_replay_buffer.append(experience)\n",
        "            \n",
        "        if done:\n",
        "            self._number_episodes += 1\n",
        "        else:\n",
        "            self._number_timesteps += 1\n",
        "            \n",
        "            # every so often the agent should learn from experiences\n",
        "            if self._number_timesteps % self._update_frequency == 0 and self.has_sufficient_experience():\n",
        "                experiences = self._experience_replay_buffer.sample()\n",
        "                self._learn_from(experiences)\n"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_00EcQkc5kE",
        "colab_type": "text"
      },
      "source": [
        "#### Epsilon decay schedule\n",
        "\n",
        "In the DQN algorithm the agent chooses its action using an $\\epsilon$-greedy policy. When using an $\\epsilon$-greedy policy, with probability $\\epsilon$, the agent explores the state space by choosing an action uniformly at random from the set of feasible actions; with probability $1-\\epsilon$, the agent exploits its current knowledge by choosing the optimal action given that current state. \n",
        "\n",
        "As the agent learns and acquires additional knowledge about it environment it makes sense to *decrease* exploration and *increase* exploitation by decreasing $\\epsilon$. In practice, it isn't a good idea to decrease $\\epsilon$ to zero; instead one typically decreases $\\epsilon$ over time according to some schedule until it reaches some minimum value.\n",
        "\n",
        "The Deepmind researchers used a simple linear decay schedule and set a minimum value of $\\epsilon=0.1$. In the cell below I code up a linear decay schedule as well as a power decay schedule that I have seen used in many other practical applications."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8k3KG-vc5kF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def linear_decay_schedule(episode_number: int,\n",
        "                          slope: float,\n",
        "                          minimum_epsilon: float) -> float:\n",
        "    \"\"\"Simple linear decay schedule used in the Deepmind paper.\"\"\"\n",
        "    return max(1 - slope * episode_number, minimum_epsilon)\n",
        "\n",
        "def power_decay_schedule(episode_number: int,\n",
        "                         decay_factor: float,\n",
        "                         minimum_epsilon: float) -> float:\n",
        "    \"\"\"Power decay schedule found in other practical applications.\"\"\"\n",
        "    return max(decay_factor**episode_number, minimum_epsilon)\n",
        "\n",
        "_epsilon_decay_schedule_kwargs = {\n",
        "    \"decay_factor\": 0.995,\n",
        "    \"minimum_epsilon\": 1e-2,\n",
        "}\n",
        "epsilon_decay_schedule = lambda n: power_decay_schedule(n, **_epsilon_decay_schedule_kwargs)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aY7N9N3uc5kN",
        "colab_type": "text"
      },
      "source": [
        "#### Choosing an optimizer\n",
        "\n",
        "As is the case in training any neural network, the choice of optimizer and the tuning of its hyper-parameters (in particular the learning rate) is important. Here I am going to more or less follow the Minh et al 2015 paper and use the [RMSProp](https://pytorch.org/docs/stable/optim.html#torch.optim.RMSprop) optimizer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lxAb-5wrc5kP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "_optimizer_kwargs = {\n",
        "    \"lr\": 5e-4,\n",
        "    \"alpha\": 0.99,\n",
        "    \"eps\": 1e-08,\n",
        "    \"weight_decay\": 0,\n",
        "    \"momentum\": 0,\n",
        "    \"centered\": False\n",
        "}\n",
        "optimizer_fn = lambda parameters: optim.RMSprop(parameters, **_optimizer_kwargs)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ex8i7ipqc5kW",
        "colab_type": "text"
      },
      "source": [
        "At this point I am ready to create an instance of the `DeepQAgent`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNFNnmAzc5kZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "_agent_kwargs = {\n",
        "    \"state_size\": env.observation_space.shape[0],\n",
        "    \"action_size\": env.action_space.n, \n",
        "    \"number_hidden_units\": 64,\n",
        "    \"optimizer_fn\": optimizer_fn,\n",
        "    \"epsilon_decay_schedule\": epsilon_decay_schedule,\n",
        "    \"batch_size\": 64,\n",
        "    \"buffer_size\": 100000,\n",
        "    \"alpha\": 1e-3,\n",
        "    \"gamma\": 0.99,\n",
        "    \"update_frequency\": 4,\n",
        "    \"seed\": None,\n",
        "}\n",
        "deep_q_agent = DeepQAgent(**_agent_kwargs)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qodMd5Y-c5kn",
        "colab_type": "text"
      },
      "source": [
        "### Performance of an un-trained `DeepQAgent`\n",
        "\n",
        "The function `simulate` defined in the cell below can be used to simuate an agent interacting with and environment for one episode."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqh4ulXBc5kq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from IPython import display\n",
        "\n",
        "\n",
        "def simulate(agent: Agent, env: gym.Env, ax: plt.Axes) -> None:\n",
        "    state = env.reset()\n",
        "    img = ax.imshow(env.render(mode='rgb_array'))\n",
        "    done = False\n",
        "    while not done:\n",
        "        action = agent.choose_action(state)\n",
        "        img.set_data(env.render(mode='rgb_array')) \n",
        "        plt.axis('off')\n",
        "        display.display(plt.gcf())\n",
        "        display.clear_output(wait=True)\n",
        "        state, reward, done, _ = env.step(action)       \n",
        "    env.close()\n",
        "    \n"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SVpPxlx2c5k1",
        "colab_type": "text"
      },
      "source": [
        "The untrained agent behaves erratically (not quite randomly!) and performs poorly. Lots of room for improvement!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DEG66kJRc5k4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "aeb6237f-ef84-43e6-f573-bcd48db87b87"
      },
      "source": [
        "_, ax = plt.subplots(1, 1, figsize=(10, 8))\n",
        "simulate(deep_q_agent, env, ax)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-OXulhCc5lJ",
        "colab_type": "text"
      },
      "source": [
        "### Training the `DeepQAgent`\n",
        "\n",
        "Now I am finally ready to train the `deep_q_agent`. The target score for the `LunarLander-v2` environment is 200 points on average for at least 100 consecutive episodes. If the `deep_q_agent` is able to \"solve\" the environment, then training will terminate early."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ciBNcVxrc5lL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "097ca1be-64ea-4980-b81e-0b76e64c2380"
      },
      "source": [
        "scores = train(deep_q_agent, env, \"checkpoint.pth\", number_episodes=2000, target_score=200)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Episode 100\tAverage Score: -167.01\n",
            "Episode 200\tAverage Score: -101.83\n",
            "Episode 300\tAverage Score: -21.46\n",
            "Episode 400\tAverage Score: 30.84\n",
            "Episode 500\tAverage Score: 138.07\n",
            "Episode 600\tAverage Score: 139.57\n",
            "Episode 700\tAverage Score: 189.45\n",
            "\n",
            "Environment solved in 748 episodes!\tAverage Score: 200.05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bT0Q4Rvic5lb",
        "colab_type": "text"
      },
      "source": [
        "### Analyzing `DeepQAgent` performance\n",
        "\n",
        "Now that I have trained the agent, let's re-run the simulation to see the difference in performance. You should see that the agent is able to pilot the lunar lander to successful landing inside the landing area (or nearby)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tlg6cRntc5ll",
        "colab_type": "text"
      },
      "source": [
        "#### Plotting the time series of scores\n",
        "\n",
        "I can use [Pandas](https://pandas.pydata.org/) to quickly plot the time series of scores along with a 100 episode moving average. Note that training stops as soon as the rolling average crosses the target score."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEJQsx-Qc5lp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CelfcJ6c5ly",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scores = pd.Series(scores, name=\"scores\")"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ta2h2tEhc5l6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "ff7516f2-474c-488b-a4b2-5adc393d5ee3"
      },
      "source": [
        "scores.describe()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    749.000000\n",
              "mean      40.669767\n",
              "std      154.187574\n",
              "min     -452.701193\n",
              "25%      -71.429054\n",
              "50%       35.203742\n",
              "75%      187.236881\n",
              "max      309.127967\n",
              "Name: scores, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95vX1gAIc5mB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "658341cd-c3a6-4b1f-d4e2-e3e1ab890819"
      },
      "source": [
        "fig= plt.figure(figsize=(10,5),dpi=500)\n",
        "\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "\n",
        "\n",
        "\n",
        "_ = scores.plot(ax=ax, label=\"Scores\")\n",
        "_ = (scores.rolling(window=100)\n",
        "           .mean()\n",
        "           .rename(\"Rolling Average\")\n",
        "           .plot(ax=ax))\n",
        "ax.axhline(200, color='k', linestyle=\"dashed\", label=\"Target Score\")\n",
        "ax.legend()\n",
        "_ = ax.set_xlabel(\"Episode Number\")\n",
        "_ = ax.set_ylabel(\"Score\")\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 5000x2500 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKZwI4NJc5mL",
        "colab_type": "text"
      },
      "source": [
        "#### Kernel density plot of the scores\n",
        "\n",
        "Kernel density plot of scores is bimodal with one mode less than -100 and a second mode greater than 200. The negative mode corresponds to those training episodes where the agent crash landed and thus scored at most -100; the positive mode corresponds to those training episodes where the agent \"solved\" the task. The kernel density or scores typically exhibits negative skewness (i.e., a fat left tail): there are lots of ways in which landing the lander can go horribly wrong (resulting in the agent getting a very low score) and only relatively few paths to a gentle landing (and a high score)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hz_4EBljc5mM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ad21ced9-5943-4c48-be04-373e4bd2a091"
      },
      "source": [
        "fig, ax = plt.subplots(1,1)\n",
        "_ = scores.plot(kind=\"kde\", ax=ax)\n",
        "_ = ax.set_xlabel(\"Score\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VRG5xiz0E5ItIFfBF3JFZqroXeAzYB/wOuEtVh4F3Ax8HrhGRHe7jJvdeXwfeKyKHgfe4z43xrcqaVpIThdXzIj/CfWVZgJbuAY63e9cxX3N6yHBkE0p+VgpgfSjxKMnLm6vqU8BTo859OeS4D/jwGNfeB9w36twrQNjFjlS1Bbh2miEbEzWVNS2sLMshPSVy/SdBK9wJjrvrTkV0SG+oI+6Q4bLcyN4/NSmRQHqy1VDiUFx2yhsT73oGhthd1+5JcxfA0tnZJCUIuzwc6VXb0kNZbnpEhwwHFWanWg0lDllCMSYG3jhyiqERjej8k1BpyU7HvJdretU2R36EV1BBVorVUOKQJRRjYmBrTQsJAmvLvUko4PSj7K73pmNeValp7o7YopCjFWan0WQ1lLhjCcWYGNhS08oFpQGyUr3rxlxRFuBUzyB1bb0Rv3dwleEFhd7VUJqthhJ3LKEYE2V9g8PsOHbKs/6ToJWlTse8F/0o1c2R20c+nMLsVLoHhukZGPLk/sYbllCMibKdx04xMDQSkf1PxrN4dhYpiQnsqo/8jPkarxOKO7mxudNmy8cTSyjGRNnWmlZEYJ2H/SfgDL9dWpLNbg9qKDXN3aQkJTAn4M2Q5ILg5MYuW3U4nlhCMSbKKmtaWVKcTSAj2fPvtaLU6ZgfGYlsx3x1Uzfl+RkkJISdFjZthaeXX7EaSjyxhGJMFA0Oj7D9SFtE9z8Zz4rSAJ19Qxxp7YnofWuauzxr7oKQ5VdspFdcsYRiTBTtrm+nd3DYs/kno61wl8WP5HyUoeERjrb2UFGQFbF7jpaXmYKIrecVbyyhGBNFW2siv6HWeBYXZ5OSlBDRpezrT/UyOKws8LCGkpyYQF5Gis2WjzOWUIyJosrqFhYWZp5eot1ryYkJLCuZFdGhw6eHDHs0ByWoICvVaihxxhKKMVEyPKJsq23jkij1nwStLAuwJ4Id8zVN3g4ZDrL1vOKPJRRjomT/iQ46+4c8n9A42orSAN0Dw6drFtNV09xNdloS+ZkpEbnfWGw9r/hjCcWYKKmMcv9J0MrgUvYRmuBY29LNgoJMnP3uvFOY7TR5eblJmIksSyjGRElldQvz8jIo8Wgy4FgWFmaSnpwYsX6U6qbuiO/SGE5hdir9QyN09dvyK/HCEooxUTAyorxe2xr15i6ApMQEls2ZFZEZ8z0DQ9Sf6mWBh0OGg2xv+fhjCcWYKDjc2EVbz2DUm7uCVpQG2Hu8g+FpdsxXNXYBsLjY+4QSnNzY3GWz5ePFpBKKiPxSRG4WEUtAxpyFrTUtAFGbIT/ayrIAvYPDvNXUNa37HGpwrl9UnB2JsMZlNZT4M9kE8V3gY8BhEfm6iCzxMCZjZpwtNa2UBNIivv/6ZK10Z8xPtx/lcEMnKYkJlOdnRCKscZ1efqXTFoiMF5NKKKr6rKr+MbAGqAWeFZE/iMifiYj3K9wZE8dUla01Tv+J1yOjxlJRkEVmSuK0Z8wfbuxiQWGmJ/vIj5abkUKCWJNXPJn0T4WI5AOfAP4ceBP4Nk6C+b0nkRkzQ9Q0d9PU2e/5/ifjSUwQlpcG2DXNNb0ONXRGpbkLnJjzbbZ8XJlsH8p/AC8DGcD7VfUWVX1UVT8LeN87Z0wcC67fdcmC2HTIB60sDbDveAeDwyNndX13/xB1bb0sLoref/nCLJstH08mu6H1v6vqU6EnRCRVVftVda0HcRkzY1TWtFKQlerpYoqTsaIsQP/QCIcbulg2Z9aUrw+O8FoUhRFeQQXZqbaEfRyZbJPX18Kcey2SgRgzU8W6/yRoRWlwKfuz60c53Bi9EV5BhdbkFVfGTSgiMltELgLSRWS1iKxxH1fhNH8ZY8ZxrLWH+lO9MW/uAijPzyQ7Nems90Y5cKKD1KQE5udF779+QbazhL0tvxIfJqqhXA/8I1AG/BPwLffxReBLE91cRG4QkYMiUiUi94R5PVVEHnVfrxSR8pDX7nXPHxSR60PO/1BEGkVkz6h7fUVE6kVkh/u4aaL4jPFatPc/GU9CgnBBaeCsZ8zvPd7B0tnZURnhFVSYlcrgsNLeOxi172nO3rg/Gar6sKpeDXxCVa8Oedyiqr8c71oRSQQeAG4ElgG3isiyUcXuANpU9TzgfuAb7rXLgI3AcuAG4Lvu/QB+7J4L535VXeU+nhqjjDFRU1nTQk5GMouLotdMNJ6VZQH2n+hkYGhqHfOqyt7j7SybE/AosvDeni1vzV7xYKImrz9xD8tF5IujHxPcex1QparVqjoAPAJsGFVmA/Cwe7wJuFachuYNwCNup38NUOXeD1V9CWid7Bs0Jpa21rRycXkeCQmx7T8JWlEWYGB4hEMNnVO6rq6tl46+IS4onXpn/nQUurPlG60fJS5MVHcNDkvJArLDPMZTChwLeV7nngtbRlWHgHYgf5LXhnO3iOxym8VywxUQkTtFZJuIbGtqaprELY05Ow0dfdS29MRkQcixrCx1lrLfcWxqHfN7jzvNZMtjVEOxjvn4MO6wYVX9vvv1b6MTzrT8G/B3gLpfvwXcPrqQqj4IPAiwdu1a6+kzngnuf3JJDCc0jjY3L52i7FS21rTyJ+vnT/q6vcc7SEwQls6ObtNdcD0vmy0fHyY7sfGbIjJLRJJF5DkRaQppDhtLPTA35HmZey5sGRFJAgJAyySvPYOqNqjqsKqOAP+O20RmTKxUVreQlZp0VnM+vCIirF+Qz5bqlimNnNpd387CwkzSkhMnLhxBgfRkkhPFaihxYrLDNa5T1Q7gfThreZ0H/I8JrnkdWCQiFSKSgtPJvnlUmc3Abe7xh4Dn1fkp3wxsdEeBVQCLgK3jfTMRKQl5+kFgz1hljYmGrTWtrC3PJdEn/SdB6xfk09jZT80ktwQeGVHePHqKNfPCtiJ7KiFBKLC5KHFjsgkl2DR2M/C4qk447tDtE7kbeBrYDzymqntF5Ksicotb7CEgX0SqcIYi3+Neuxd4DNgH/A64S1WHAUTkFziTKpeISJ2I3OHe65sisltEdgFXA1+Y5HszJuJauvo53Njlq+auoEsXOjFtqZ7c2Jbq5i7aewdZMz/6CQWgKDuVRltxOC5MdumV34jIAaAX+LSIFAIT/gu7Q3efGnXuyyHHfcCHx7j2PuC+MOdvHaP8xyeKx5hoeb3WP/NPRivPz6B4Vipbqlv42CXzJiy//UgbABfFKKEUZqdS19Ybk+9tpmayy9ffA7wLWKuqg0A37xwCbIxxbaluJT058fRyJ34S7Ed5bZL9KNuPtJGbkRyztcgKs9OsyStOTGXK61LgoyLypzj9Hdd5E5Ix8W9rTStr5ueQkuTPTU6vWFRIU2c/e493TFh225E2LpqfG7O1yIqyU2npHjjrVZJN9Ex2lNdPcZZguQy42H3YKsPGhNHeM8j+kx2+7D8JumpJISLw3P7GccudaO+luqk7pk13wbkoLTZ02Pcm24eyFlimtkKbMRPadqQVVX/2nwTlZ6Wyem4Ozx9o4HPvWTRmuZcPNQNwxeLCaIX2DkXZwdnyfcwOpMUsDjOxydbH9wCzvQzEmJmisqaVlMQEVs3NiXUo47r2/GJ21rXT0DH2+JoXDzdRlJ3KkiguWT9a0SwniVg/iv9NNqEUAPtE5GkR2Rx8eBmYMfGqsqaVVXNzoj4JcKpuvMD5G/HJHeHnDPcNDvPSwSauXFwY071cCrNtPa94Mdkmr694GYQxM0VX/xB76tv5zFULYx3KhBYUZrF6Xg6bttfxF5cveEfSePFQE539Q9y8smSMO0TH6QUiOyyh+N1khw2/iDNDPtk9fh14w8O4jIlLbxxpY3hEfd1/EuqP1pRxqKEr7KZbm3ceJzcjmXefVxCDyN6WkpRAbkYyTV02udHvJjvK6y9wlpf/vnuqFPiVV0EZE68qa1pISpCYTQKcqvdfOIfMlEQefKn6jPPHT/Xyuz0n+eDqMpKjuKHWWAqzU62GEgcm+5NyF/BuoANAVQ8DRV4FZUy82lrTygWlATJSJtuaHFuB9GRue1c5/7n7xOkl6oHTCeb2y8pjFNmZirLTrA8lDkw2ofS7m2QBp1cGtiHExoToGxxm57F2X+wfPxV/cfkC8jNT+cKjO+joG2RLdQs/3XKEj6ydS1lu9PaPH09hti0QGQ8m+2fUiyLyJSBdRN4LfAb4tXdhGRN/3jx6ioHhEV9tqDUZuZkp/NNHLuT2H7/OZV9/nu6BYcrzM7jnxqWxDu20IjehqGpMR5yZ8U02odyDs//7buCTOAs+/sCroIyJR5U1LYjA2vL4SijgTFx89JPr+XnlUfIzU/jUlQsJpCfHOqzTCrNTGRgeob13kJyMlFiHY8YwqYSiqiMi8ivgV6pq++YaE8bWmlaWlcxiVpp/fhFPxUXz87hovj+TYehWwJZQ/GvcPhRxfEVEmoGDwEF3t8Yvj3edMeeagaER3jja5uv1u+JZUbYzW9465v1tok75L+CM7rpYVfNUNQ+4BHi3iNgGVsa4dtefom9wJG7mn8Sbollv11CMf02UUD4O3KqqNcETqloN/Anwp14GZkw8Ce5+aAnFG4UhC0Qa/5oooSSravPok24/Snw2FBvjga01rSwuziIv09r3vZCdmkRacoJNbvS5iRLKeBsQ2OYExgBDwyNsq221/hMPiQhF2Wk0dVlC8bOJRnldKCLhtnQTwDYmMAbYd6KD7oFha+7ymC2/4n/jJhRV9ff628b4QKXbfxJvExrjTVF2KocaOmMdhhlH7Fd9MybOVda0UlGQeXojKOONIlt+xfcsoRgzDSMjyuu1rVY7iYKiWWl09A3RNzgc61DMGCyhGDMNBxs6ae8dtP6TKCh2a4An223osF9ZQjFmGiqrWwC4ZIGN8PJaScBJKCcsofiWpwlFRG4QkYMiUiUi94R5PVVEHnVfrxSR8pDX7nXPHxSR60PO/1BEGkVkz6h75YnI70XksPs1PnY4MnFta20rpTnplOakxzqUGW+2m1AaOiyh+JVnCUVEEoEHgBuBZcCtIrJsVLE7gDZVPQ+4H/iGe+0yYCOwHLgB+K57P4Afu+dGuwd4TlUXAc+5z43xjKqytaY17vY/iVezZ1kNxe+8rKGsA6pUtdrdnOsRYMOoMhuAh93jTcC14mx2sAF4RFX73WVfqtz7oaovAa1hvl/ovR4GPhDJN2PMaG81ddPcNWAd8lGSmZpEdloSJ9t7Yx2KGYOXCaUUOBbyvM49F7aMqg4B7UD+JK8drVhVT7jHJ4HiswvbmMnZ4vafrLf+k6gpCaRZDcXHZmSnvKoqY2xRLCJ3isg2EdnW1GRbu5izt6W6hZJAGvPy/LFN7rlgdiDd+lB8zMuEUg/MDXle5p4LW8bdpz4AtEzy2tEaRKTEvVcJ0BiukKo+qKprVXVtYWHhJN+KMWdSVbZUt7J+Qb5tSRtFJbOshuJnXiaU14FFIlIhIik4neybR5XZDNzmHn8IeN6tXWwGNrqjwCqARcDWCb5f6L1uA56MwHswJqy3mrpo7upnvXXIR1VxwFkgcnB4JNahmDA8Syhun8jdwNPAfuAxVd0rIl8VkVvcYg8B+SJSBXwRd2SWqu4FHgP2Ab8D7lLVYQAR+QXwGrBEROpE5A73Xl8H3isih4H3uM+N8cRr7vpd1n8SXSWBNFRtoy2/mtSe8mdLVZ8Cnhp17sshx33Ah8e49j7gvjDnbx2jfAtw7XTiNWayrP8kNmaHTG6cY3N/fGdGdsob4yVVpbK6xfpPYiA4W96WX/EnSyjGTJHTfzJg/Scx8PbkRpuL4keWUIyZotfecuafXLqgIMaRnHsC6cmkJSfY0GGfsoRizBRtqW5lTiCNuXnWhh9tIkJJIN2GDvuUJRRjpsCZf2L9J7E0e1aa9aH4lCUUY6bgcGMXLd0DNlw4hmz5Ff+yhGLMFLxyuBmASxdaQomVstx0Tnb0MWSTG33HEooxU/Dy4SYqCjKZa/NPYqY0N53hEbVaig9ZQjFmkvqHhtlS3cpl59norlgqy3WSeV2bDR32G0soxkzSG0dO0Ts4zOWLLKHEUlmuM7qurq0nxpGY0SyhGDNJLx9uIjFBrP8kxkoC6YhYDcWPLKEYM0mvVDWzem4O2WnJsQ7lnJaSlEBxdhr1pyyh+I0lFAiMlgQAABPCSURBVGMmoa17gN317Vy+yPbQ8YOy3HRr8vIhSyjGTMKrbzWjCpdZ/4kvOAnFaih+YwnFmEl4+VAz2WlJXFgWiHUoBmfo8Il2m4viN5ZQjJnAyIjy/MFGLl9UQFKi/Zfxg7LcDIZHlAbbaMtX7H+HMRPYXd9OU2c/7zm/ONahGNfpocOt1o/iJ5ZQjJnAc/sbSBC4eklRrEMxruDkxmPWj+IrllCMmcCz+xu5aH4uuZkpsQ7FuEpz0kkQONLSHetQTAhLKMaM4/ipXvad6OBaa+7ylZSkBMpyM6hutoTiJ5ZQjBnHcwcaAXjP+dbc5TcVBZnUWkLxFUsoxozjuf0NzM/PYGFhVqxDMaMEE4qqxjoU47KEYswY2nsGebWqmeuWFdvujD5Unp9B98AwTV02dNgvLKEYM4Zn9p1kcFh538o5sQ7FhFFekAlAbbMNHfYLSyjGjOE3u05QlpvOSpsd70sVpxOK9aP4hSUUY8Jo6x7g1apmbl5ZYs1dPlWak05SglBjQ4d9w9OEIiI3iMhBEakSkXvCvJ4qIo+6r1eKSHnIa/e65w+KyPUT3VNEfiwiNSKyw32s8vK9mZntmX0nGRpR3rfCmrv8KikxgXl5GVZD8ZEkr24sIonAA8B7gTrgdRHZrKr7QordAbSp6nkishH4BvBREVkGbASWA3OAZ0VksXvNePf8H6q6yav3ZM4dv3rzOPPzM7igdFasQzHjqCjIpMYSim94WUNZB1SparWqDgCPABtGldkAPOwebwKuFad9YQPwiKr2q2oNUOXebzL3NGZajrb08Fp1Cx9aU2bNXT63oNBJKMMjNnTYD7xMKKXAsZDnde65sGVUdQhoB/LHuXaie94nIrtE5H4RSQ0XlIjcKSLbRGRbU1PT1N+VmfE2bT+GCPzRRWWxDsVMYHFxNv1DIxy1RSJ9YSZ1yt8LLAUuBvKA/xWukKo+qKprVXVtYaHtvmfONDyibNpex+WLCpmTkx7rcMwElszOBuDgyc4YR2LA24RSD8wNeV7mngtbRkSSgADQMs61Y95TVU+oox/4EU7zmDFT8mpVM8fb+/jIWqudxIPzipwVDA41WELxAy8TyuvAIhGpEJEUnE72zaPKbAZuc48/BDyvzjoKm4GN7iiwCmARsHW8e4pIiftVgA8Aezx8b2aG+slrteRlptjeJ3EiIyWJeXkZllB8wrNRXqo6JCJ3A08DicAPVXWviHwV2Kaqm4GHgJ+KSBXQipMgcMs9BuwDhoC7VHUYINw93W/5cxEpBATYAXzKq/dmZqaa5m6eO9DIZ69ZRFpyYqzDMZO0uDjbEopPeJZQAFT1KeCpUee+HHLcB3x4jGvvA+6bzD3d89dMN15zbvvRqzUkJyTw8fXzYx2KmYIls7N44WAjA0MjpCTNpG7h+GOfvjFAc1c/j2+r45ZVcyjMDjtA0PjUktmzGBpRDjdaLSXWLKEYA3zvhbfoHxrmM1ctjHUoZopWlDprre2pb49xJMYSijnnnWzv46dbjvBHa8pYYPuexJ35eRlkpyWxq84SSqxZQjHnvH985iDDI8pfXrso1qGYs5CQIKwoDbDbaigxZwnFnNO2VLewaXsdf3HFAubmZcQ6HHOWVpQGOHCik4GhkViHck6zhGLOWX2Dw/zvX+2hLDedv7zGaifxbEVZgIHhERs+HGOWUMw5629/vY+qxi7u++AK0lNs3kk8u7AsB4A3jrbFOJJzmyUUc0762ZYj/GLrUT515UKuXGxrusW7stx0SgJpVNa0xjqUc5olFHPOeWJ7HX/z5B6uXVrEf79u8cQXGN8TEdZV5LG1phVn9SYTC5ZQzDljYGiEf3j6AH/1+E4uXZDPv35sDUmJ9l9gprikIp+mzn5qW2wp+1jxdOkVY/xAVXnhUBPf+O0BDpzs5CNry/jaB1bYMh0zzLqKPAC21rRQUZAZ42jOTZZQzIykquw/0clv95zgP3efoLqpm9KcdP79T9fy3mW2kvBMtLAwk8LsVF4+3MxHL54X63DOSZZQzIxy4GQHv955nP/cdYLalh4SxGkK+fSVC/nA6lKSrYlrxhIRrl5SyG/3nGRweMT+rWPAEoqJe32Dw/zHm/X8+NVaDjZ0kpggXLognzuvWMh1y4spyLLFHs8V1ywt5rFtdWyrbePShfmxDuecYwnFxC1V5Zdv1PN/f7uf5q4Bls+Zxd9tWM6NK0osiZyjLl9UQEpiAs/tb7CEEgOWUExcOtbaw5f+YzcvH27movm5fOfW1Vy6IB9nw05zrspMTeLShfn8ds9JvnTT+SQk2M9DNFkjo4krwyPKD16u5rr7X+KNI2383YblPP7JS3nXwgJLJgaAD64upf5UL1trbZJjtFkNxcSN/Sc6uOeJXeysa+eapUV87QMXMCcnPdZhGZ+5fvlsslKTeGJ7HesXWLNXNFkNxfhe3+Aw33rmIO//l1eoa+vlO7eu5qHb1loyMWGlpyRy04rZPLX7BO29g7EO55xiCcX4WmV1Czd/52X+5fkqblk1h2e/eCW3XDjHmrfMuP700nK6B4b52ZYjsQ7lnGJNXsaXWrr6+funDvDEG3WU5qTz8O3rbBFHM2kXlAa4cnEhP3q1htvfXWGrSUeJ1VCMr/QODPP9F9/imm+9yJM76vnMVQt59otXWjIxU3b3NefR3DXA9196K9ahnDOshmJ8obt/iMe3HeOBF96iqbOfKxYX8jc3n8+i4uxYh2bi1MXlebz/wjl894W3+MCqUsptfS/PWUIxMXW4oZOfVx7lie11dPYPsa4ijwc+tub0Qn/GTMf/vvl8XjjYyN2/eINNn3oXacnW9OUlSygm6o60dPObXSf49c7jHDjZSXKicNOKEj7xrnJWz8uNdXhmBimelcY/f3QVdzy8jS88uoNvb1xtq0x7yBKK8VxX/xCV1S28dKiJlw43U9PcDcCaeTn8n/cv4+aVJRRlp8U4SjNTXXt+MV9+3zK++pt9dD38Ovd/dJUtzeMRTxOKiNwAfBtIBH6gql8f9Xoq8BPgIqAF+Kiq1rqv3QvcAQwDf6mqT493TxGpAB4B8oHtwMdVdcDL92feqW9wmLeauthb38Gbx9p48+gpDjZ0ogppyQmsX5DPx9fP573LipmblxHrcM054vbLKshMTeRvntzLdfe/xKevXMitl8wjK9X+po4k8Wq7TBFJBA4B7wXqgNeBW1V1X0iZzwArVfVTIrIR+KCqflRElgG/ANYBc4BngeBerWHvKSKPAb9U1UdE5HvATlX9t/FiXLt2rW7bti2C73rmGhgaoat/iO7+Ibr6h2jrGaCho4+T7f00dPRxtLWHqsYujrX1EPyRmpWWxKp5uayem8PF5XmsLc+1NmwTUwdOdvC13+znlapmUpMSuHxRAWvm53J+ySzmBNKZPSuNrLQkEm0NsHGJyHZVXTv6vJfpeR1QparVbgCPABuAfSFlNgBfcY83Af8qzoy1DcAjqtoP1IhIlXs/wt1TRPYD1wAfc8s87N533IRytr7z3GE27zx+eu/q0ylZOfM5vKOMni6jZz4flddDE/2E1456nVGvn1lmjHjGeS8DQyMMDI8wluzUJEpz01lRFuC/rSnlvKIsls7OZkFBli3OZ3xl6exZ/OzPL+HNo208ueM4Lxxs5Nn9je8ol5KUQEZKImlJiSSIs9eKCCSM+ioQt5Ns//6DKyI++MXLhFIKHAt5XgdcMlYZVR0SkXacJqtSYMuoa0vd43D3zAdOqepQmPJnEJE7gTsB5s07u13dirJTWRIczipnfDn9wxX6IyYTlTn9uoQtf+a5UWVG3eSd3yvkHmOWOfM/xOjvlZKUQFZqIpmpSWS5j0B6MsWBNGbPSiPTmg1MnFk9L9cdALKc9p5BDjd2crKjj5PtfXT1D9E7MEzv4DB9g8OMqPOHl6rzp9yIqntO3/GHYDzJTI18a8E595tAVR8EHgSnyets7rFx3Tw2rrMtRo2ZCQIZyawtt2HqkeDl+Ll6YG7I8zL3XNgyIpIEBHA658e6dqzzLUCOe4+xvpcxxhgPeZlQXgcWiUiFiKQAG4HNo8psBm5zjz8EPK9OY/5mYKOIpLqjtxYBW8e6p3vNf7n3wL3nkx6+N2OMMaN41uTl9oncDTyNM8T3h6q6V0S+CmxT1c3AQ8BP3U73VpwEgVvuMZwO/CHgLlUdBgh3T/db/i/gERH5GvCme29jjDFR4tmw4Xhgw4aNMWbqxho2bGsQGGOMiQhLKMYYYyLCEooxxpiIsIRijDEmIs7pTnkRaQK82HS6AGj24L6RYvFNj8U3PRbf9Pghvvmq+o5tVM/phOIVEdkWbgSEX1h802PxTY/FNz1+js+avIwxxkSEJRRjjDERYQnFGw/GOoAJWHzTY/FNj8U3Pb6Nz/pQjDHGRITVUIwxxkSEJRRjjDERYQllGkRklYhsEZEdIrJNRNa550VEviMiVSKyS0TWhFxzm4gcdh+3jX33iMX4WRE5ICJ7ReSbIefvdeM7KCLXh5y/wT1XJSL3eB2f+z3/SkRURArc5774/ETkH9zPbpeI/IeI5IS85pvPzw/fOySGuSLyXyKyz/2Z+5x7Pk9Efu/+u/1eRHLd82P+W3scZ6KIvCkiv3GfV4hIpRvHo+72GLhbaDzqnq8UkfIoxJYjIpvcn739InKp3z6/MTnbWNrjbB7AM8CN7vFNwAshx7/F2WV3PVDpns8Dqt2vue5xrofxXQ08C6S6z4vcr8uAnUAqUAG8hbMdQKJ7vABIccss8/gznIuzHcERoMBnn991QJJ7/A3gG377/EJijdn3HhVHCbDGPc4GDrmf1zeBe9zz94R8lmH/raMQ5xeB/wf8xn3+GLDRPf4e8Gn3+DPA99zjjcCjUYjtYeDP3eMUIMdvn99YD6uhTI8Cs9zjAHDcPd4A/EQdW3B2kywBrgd+r6qtqtoG/B64wcP4Pg18XVX7AVS1MSS+R1S1X1VrgCpgnfuoUtVqVR0AHnHLeul+4H/ifJZBvvj8VPUZVR1yn27B2Qk0GJ9fPr+gWH7v01T1hKq+4R53AvuBUjeWh91iDwMfcI/H+rf2jIiUATcDP3CfC3ANsGmM+IJxbwKudct7FVsAuAJ3PydVHVDVU/jo8xuPJZTp+TzwDyJyDPhH4F73fClwLKRcnXturPNeWQxc7lbVXxSRi/0Un4hsAOpVdeeol3wR3yi34/wlyDhxxDK+WH7vsNzmodVAJVCsqifcl04Cxe5xLOL+Z5w/Ykbc5/nAqZA/HkJjOB2f+3q7W94rFUAT8CO3Se4HIpKJvz6/MXm2Y+NMISLPArPDvPTXwLXAF1T1CRH5CM5fFe/xUXxJOM1D64GLgcdEZEEUw5sovi/hNCvFzHjxqeqTbpm/xtk59OfRjC2eiUgW8ATweVXtCP2jXlVVRGIyX0FE3gc0qup2EbkqFjFMIAlYA3xWVStF5Ns4TVynxfLzm4gllAmo6pgJQkR+AnzOffo4bhUaqMfpGwgqc8/VA1eNOv+Ch/F9GvilOo2tW0VkBGdhubHiY5zzEY1PRFbg/DW20/1lUwa8Ic7ABl98fm6cnwDeB1zrfo6MEx/jnPfaeDFFlYgk4ySTn6vqL93TDSJSoqon3CaZYPNrtON+N3CLiNwEpOE0WX8bp6koya2FhMYQjK9ORJJwmrZbPIyvDqhT1Ur3+SachOKXz298sezAifcHTvvwVe7xtcB29/hmzuwo2+qezwNqcDqUc93jPA/j+xTwVfd4MU7VWIDlnNmpXI3TqZvkHlfwdsfu8ih9lrW83Snvl8/vBmAfUDjqvB8/v5h971FxCPAT4J9Hnf8HzuxU/uZ4/9ZRivUq3u6Uf5wzO+U/4x7fxZmd8o9FIa6XgSXu8Vfcz853n1/Y2GP5zeP9AVwGbHf/81YCF7nnBXgAZ9TNbmBtyDW343TiVgF/5nF8KcDPgD3AG8A1Ia/9tRvfQdyRau75m3BG5ryF0+wTrc8yNKH45fOrwknCO9zH9/z6+cX6e4fEcBnOAItdIZ/bTTj9Ds8Bh3FGHuZN9G8dhVhDE8oCYKv7b/44b4+MTHOfV7mvL4hCXKuAbe5n+CucP5589/mFe9jSK8YYYyLCRnkZY4yJCEsoxhhjIsISijHGmIiwhGKMMSYiLKEYY4yJCEsoxkSBiPy1u/ruLnFWp74k1jEZE2k2U94Yj4nIpTiz7deoar84y/SnTON+wRndxviK1VCM8V4J0Kxvr/rcrKrHReRiEfmDiOwUka0iki0iaSLyIxHZ7S4OeDU4S8CIyGYReR54TkQyReSH7nVvugttGhNTVkMxxnvPAF8WkUM4s5wfBV5zv35UVV8XkVlAL87acKqqK0RkKfCMiCx277MGWKmqrSLy98Dzqnq7OBt/bRWRZ1W1O9pvzpggq6EY4zFV7QIuAu7EWZr8UeCTwAlVfd0t0+E2Y12Gs1wOqnoAZ+OxYEL5vaq2usfXAfeIyA6cBTLTgHlReUPGjMFqKMZEgaoO4/zif0FEduMsOjhVobUPAf5IVQ9GIDxjIsJqKMZ4TESWiMiikFOrcFaqLglueub2nyThrDT7x+65xTi1jnBJ42ngs8HdA0VktYdvwZhJsRqKMd7LAv7F7esYwlm59k7gR+75dJz+k/cA3wX+za3FDAGfcEeGjb7n3+HsPLhLRBJwlvJ/XzTejDFjsdWGjTHGRIQ1eRljjIkISyjGGGMiwhKKMcaYiLCEYowxJiIsoRhjjIkISyjGGGMiwhKKMcaYiPj/6zX7mNScXl4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VRG5xiz0E5ItIFfBF3JFZqroXeAzYB/wOuEtVh4F3Ax8HrhGRHe7jJvdeXwfeKyKHgfe4z43xrcqaVpIThdXzIj/CfWVZgJbuAY63e9cxX3N6yHBkE0p+VgpgfSjxKMnLm6vqU8BTo859OeS4D/jwGNfeB9w36twrQNjFjlS1Bbh2miEbEzWVNS2sLMshPSVy/SdBK9wJjrvrTkV0SG+oI+6Q4bLcyN4/NSmRQHqy1VDiUFx2yhsT73oGhthd1+5JcxfA0tnZJCUIuzwc6VXb0kNZbnpEhwwHFWanWg0lDllCMSYG3jhyiqERjej8k1BpyU7HvJdretU2R36EV1BBVorVUOKQJRRjYmBrTQsJAmvLvUko4PSj7K73pmNeValp7o7YopCjFWan0WQ1lLhjCcWYGNhS08oFpQGyUr3rxlxRFuBUzyB1bb0Rv3dwleEFhd7VUJqthhJ3LKEYE2V9g8PsOHbKs/6ToJWlTse8F/0o1c2R20c+nMLsVLoHhukZGPLk/sYbllCMibKdx04xMDQSkf1PxrN4dhYpiQnsqo/8jPkarxOKO7mxudNmy8cTSyjGRNnWmlZEYJ2H/SfgDL9dWpLNbg9qKDXN3aQkJTAn4M2Q5ILg5MYuW3U4nlhCMSbKKmtaWVKcTSAj2fPvtaLU6ZgfGYlsx3x1Uzfl+RkkJISdFjZthaeXX7EaSjyxhGJMFA0Oj7D9SFtE9z8Zz4rSAJ19Qxxp7YnofWuauzxr7oKQ5VdspFdcsYRiTBTtrm+nd3DYs/kno61wl8WP5HyUoeERjrb2UFGQFbF7jpaXmYKIrecVbyyhGBNFW2siv6HWeBYXZ5OSlBDRpezrT/UyOKws8LCGkpyYQF5Gis2WjzOWUIyJosrqFhYWZp5eot1ryYkJLCuZFdGhw6eHDHs0ByWoICvVaihxxhKKMVEyPKJsq23jkij1nwStLAuwJ4Id8zVN3g4ZDrL1vOKPJRRjomT/iQ46+4c8n9A42orSAN0Dw6drFtNV09xNdloS+ZkpEbnfWGw9r/hjCcWYKKmMcv9J0MrgUvYRmuBY29LNgoJMnP3uvFOY7TR5eblJmIksSyjGRElldQvz8jIo8Wgy4FgWFmaSnpwYsX6U6qbuiO/SGE5hdir9QyN09dvyK/HCEooxUTAyorxe2xr15i6ApMQEls2ZFZEZ8z0DQ9Sf6mWBh0OGg2xv+fhjCcWYKDjc2EVbz2DUm7uCVpQG2Hu8g+FpdsxXNXYBsLjY+4QSnNzY3GWz5ePFpBKKiPxSRG4WEUtAxpyFrTUtAFGbIT/ayrIAvYPDvNXUNa37HGpwrl9UnB2JsMZlNZT4M9kE8V3gY8BhEfm6iCzxMCZjZpwtNa2UBNIivv/6ZK10Z8xPtx/lcEMnKYkJlOdnRCKscZ1efqXTFoiMF5NKKKr6rKr+MbAGqAWeFZE/iMifiYj3K9wZE8dUla01Tv+J1yOjxlJRkEVmSuK0Z8wfbuxiQWGmJ/vIj5abkUKCWJNXPJn0T4WI5AOfAP4ceBP4Nk6C+b0nkRkzQ9Q0d9PU2e/5/ifjSUwQlpcG2DXNNb0ONXRGpbkLnJjzbbZ8XJlsH8p/AC8DGcD7VfUWVX1UVT8LeN87Z0wcC67fdcmC2HTIB60sDbDveAeDwyNndX13/xB1bb0sLoref/nCLJstH08mu6H1v6vqU6EnRCRVVftVda0HcRkzY1TWtFKQlerpYoqTsaIsQP/QCIcbulg2Z9aUrw+O8FoUhRFeQQXZqbaEfRyZbJPX18Kcey2SgRgzU8W6/yRoRWlwKfuz60c53Bi9EV5BhdbkFVfGTSgiMltELgLSRWS1iKxxH1fhNH8ZY8ZxrLWH+lO9MW/uAijPzyQ7Nems90Y5cKKD1KQE5udF779+QbazhL0tvxIfJqqhXA/8I1AG/BPwLffxReBLE91cRG4QkYMiUiUi94R5PVVEHnVfrxSR8pDX7nXPHxSR60PO/1BEGkVkz6h7fUVE6kVkh/u4aaL4jPFatPc/GU9CgnBBaeCsZ8zvPd7B0tnZURnhFVSYlcrgsNLeOxi172nO3rg/Gar6sKpeDXxCVa8Oedyiqr8c71oRSQQeAG4ElgG3isiyUcXuANpU9TzgfuAb7rXLgI3AcuAG4Lvu/QB+7J4L535VXeU+nhqjjDFRU1nTQk5GMouLotdMNJ6VZQH2n+hkYGhqHfOqyt7j7SybE/AosvDeni1vzV7xYKImrz9xD8tF5IujHxPcex1QparVqjoAPAJsGFVmA/Cwe7wJuFachuYNwCNup38NUOXeD1V9CWid7Bs0Jpa21rRycXkeCQmx7T8JWlEWYGB4hEMNnVO6rq6tl46+IS4onXpn/nQUurPlG60fJS5MVHcNDkvJArLDPMZTChwLeV7nngtbRlWHgHYgf5LXhnO3iOxym8VywxUQkTtFZJuIbGtqaprELY05Ow0dfdS29MRkQcixrCx1lrLfcWxqHfN7jzvNZMtjVEOxjvn4MO6wYVX9vvv1b6MTzrT8G/B3gLpfvwXcPrqQqj4IPAiwdu1a6+kzngnuf3JJDCc0jjY3L52i7FS21rTyJ+vnT/q6vcc7SEwQls6ObtNdcD0vmy0fHyY7sfGbIjJLRJJF5DkRaQppDhtLPTA35HmZey5sGRFJAgJAyySvPYOqNqjqsKqOAP+O20RmTKxUVreQlZp0VnM+vCIirF+Qz5bqlimNnNpd387CwkzSkhMnLhxBgfRkkhPFaihxYrLDNa5T1Q7gfThreZ0H/I8JrnkdWCQiFSKSgtPJvnlUmc3Abe7xh4Dn1fkp3wxsdEeBVQCLgK3jfTMRKQl5+kFgz1hljYmGrTWtrC3PJdEn/SdB6xfk09jZT80ktwQeGVHePHqKNfPCtiJ7KiFBKLC5KHFjsgkl2DR2M/C4qk447tDtE7kbeBrYDzymqntF5Ksicotb7CEgX0SqcIYi3+Neuxd4DNgH/A64S1WHAUTkFziTKpeISJ2I3OHe65sisltEdgFXA1+Y5HszJuJauvo53Njlq+auoEsXOjFtqZ7c2Jbq5i7aewdZMz/6CQWgKDuVRltxOC5MdumV34jIAaAX+LSIFAIT/gu7Q3efGnXuyyHHfcCHx7j2PuC+MOdvHaP8xyeKx5hoeb3WP/NPRivPz6B4Vipbqlv42CXzJiy//UgbABfFKKEUZqdS19Ybk+9tpmayy9ffA7wLWKuqg0A37xwCbIxxbaluJT058fRyJ34S7Ed5bZL9KNuPtJGbkRyztcgKs9OsyStOTGXK61LgoyLypzj9Hdd5E5Ix8W9rTStr5ueQkuTPTU6vWFRIU2c/e493TFh225E2LpqfG7O1yIqyU2npHjjrVZJN9Ex2lNdPcZZguQy42H3YKsPGhNHeM8j+kx2+7D8JumpJISLw3P7GccudaO+luqk7pk13wbkoLTZ02Pcm24eyFlimtkKbMRPadqQVVX/2nwTlZ6Wyem4Ozx9o4HPvWTRmuZcPNQNwxeLCaIX2DkXZwdnyfcwOpMUsDjOxydbH9wCzvQzEmJmisqaVlMQEVs3NiXUo47r2/GJ21rXT0DH2+JoXDzdRlJ3KkiguWT9a0SwniVg/iv9NNqEUAPtE5GkR2Rx8eBmYMfGqsqaVVXNzoj4JcKpuvMD5G/HJHeHnDPcNDvPSwSauXFwY071cCrNtPa94Mdkmr694GYQxM0VX/xB76tv5zFULYx3KhBYUZrF6Xg6bttfxF5cveEfSePFQE539Q9y8smSMO0TH6QUiOyyh+N1khw2/iDNDPtk9fh14w8O4jIlLbxxpY3hEfd1/EuqP1pRxqKEr7KZbm3ceJzcjmXefVxCDyN6WkpRAbkYyTV02udHvJjvK6y9wlpf/vnuqFPiVV0EZE68qa1pISpCYTQKcqvdfOIfMlEQefKn6jPPHT/Xyuz0n+eDqMpKjuKHWWAqzU62GEgcm+5NyF/BuoANAVQ8DRV4FZUy82lrTygWlATJSJtuaHFuB9GRue1c5/7n7xOkl6oHTCeb2y8pjFNmZirLTrA8lDkw2ofS7m2QBp1cGtiHExoToGxxm57F2X+wfPxV/cfkC8jNT+cKjO+joG2RLdQs/3XKEj6ydS1lu9PaPH09hti0QGQ8m+2fUiyLyJSBdRN4LfAb4tXdhGRN/3jx6ioHhEV9tqDUZuZkp/NNHLuT2H7/OZV9/nu6BYcrzM7jnxqWxDu20IjehqGpMR5yZ8U02odyDs//7buCTOAs+/sCroIyJR5U1LYjA2vL4SijgTFx89JPr+XnlUfIzU/jUlQsJpCfHOqzTCrNTGRgeob13kJyMlFiHY8YwqYSiqiMi8ivgV6pq++YaE8bWmlaWlcxiVpp/fhFPxUXz87hovj+TYehWwJZQ/GvcPhRxfEVEmoGDwEF3t8Yvj3edMeeagaER3jja5uv1u+JZUbYzW9465v1tok75L+CM7rpYVfNUNQ+4BHi3iNgGVsa4dtefom9wJG7mn8Sbollv11CMf02UUD4O3KqqNcETqloN/Anwp14GZkw8Ce5+aAnFG4UhC0Qa/5oooSSravPok24/Snw2FBvjga01rSwuziIv09r3vZCdmkRacoJNbvS5iRLKeBsQ2OYExgBDwyNsq221/hMPiQhF2Wk0dVlC8bOJRnldKCLhtnQTwDYmMAbYd6KD7oFha+7ymC2/4n/jJhRV9ff628b4QKXbfxJvExrjTVF2KocaOmMdhhlH7Fd9MybOVda0UlGQeXojKOONIlt+xfcsoRgzDSMjyuu1rVY7iYKiWWl09A3RNzgc61DMGCyhGDMNBxs6ae8dtP6TKCh2a4An223osF9ZQjFmGiqrWwC4ZIGN8PJaScBJKCcsofiWpwlFRG4QkYMiUiUi94R5PVVEHnVfrxSR8pDX7nXPHxSR60PO/1BEGkVkz6h75YnI70XksPs1PnY4MnFta20rpTnplOakxzqUGW+2m1AaOiyh+JVnCUVEEoEHgBuBZcCtIrJsVLE7gDZVPQ+4H/iGe+0yYCOwHLgB+K57P4Afu+dGuwd4TlUXAc+5z43xjKqytaY17vY/iVezZ1kNxe+8rKGsA6pUtdrdnOsRYMOoMhuAh93jTcC14mx2sAF4RFX73WVfqtz7oaovAa1hvl/ovR4GPhDJN2PMaG81ddPcNWAd8lGSmZpEdloSJ9t7Yx2KGYOXCaUUOBbyvM49F7aMqg4B7UD+JK8drVhVT7jHJ4HiswvbmMnZ4vafrLf+k6gpCaRZDcXHZmSnvKoqY2xRLCJ3isg2EdnW1GRbu5izt6W6hZJAGvPy/LFN7rlgdiDd+lB8zMuEUg/MDXle5p4LW8bdpz4AtEzy2tEaRKTEvVcJ0BiukKo+qKprVXVtYWHhJN+KMWdSVbZUt7J+Qb5tSRtFJbOshuJnXiaU14FFIlIhIik4neybR5XZDNzmHn8IeN6tXWwGNrqjwCqARcDWCb5f6L1uA56MwHswJqy3mrpo7upnvXXIR1VxwFkgcnB4JNahmDA8Syhun8jdwNPAfuAxVd0rIl8VkVvcYg8B+SJSBXwRd2SWqu4FHgP2Ab8D7lLVYQAR+QXwGrBEROpE5A73Xl8H3isih4H3uM+N8cRr7vpd1n8SXSWBNFRtoy2/mtSe8mdLVZ8Cnhp17sshx33Ah8e49j7gvjDnbx2jfAtw7XTiNWayrP8kNmaHTG6cY3N/fGdGdsob4yVVpbK6xfpPYiA4W96WX/EnSyjGTJHTfzJg/Scx8PbkRpuL4keWUIyZotfecuafXLqgIMaRnHsC6cmkJSfY0GGfsoRizBRtqW5lTiCNuXnWhh9tIkJJIN2GDvuUJRRjpsCZf2L9J7E0e1aa9aH4lCUUY6bgcGMXLd0DNlw4hmz5Ff+yhGLMFLxyuBmASxdaQomVstx0Tnb0MWSTG33HEooxU/Dy4SYqCjKZa/NPYqY0N53hEbVaig9ZQjFmkvqHhtlS3cpl59norlgqy3WSeV2bDR32G0soxkzSG0dO0Ts4zOWLLKHEUlmuM7qurq0nxpGY0SyhGDNJLx9uIjFBrP8kxkoC6YhYDcWPLKEYM0mvVDWzem4O2WnJsQ7lnJaSlEBxdhr1pyyh+I0lFAiMlgQAABPCSURBVGMmoa17gN317Vy+yPbQ8YOy3HRr8vIhSyjGTMKrbzWjCpdZ/4kvOAnFaih+YwnFmEl4+VAz2WlJXFgWiHUoBmfo8Il2m4viN5ZQjJnAyIjy/MFGLl9UQFKi/Zfxg7LcDIZHlAbbaMtX7H+HMRPYXd9OU2c/7zm/ONahGNfpocOt1o/iJ5ZQjJnAc/sbSBC4eklRrEMxruDkxmPWj+IrllCMmcCz+xu5aH4uuZkpsQ7FuEpz0kkQONLSHetQTAhLKMaM4/ipXvad6OBaa+7ylZSkBMpyM6hutoTiJ5ZQjBnHcwcaAXjP+dbc5TcVBZnUWkLxFUsoxozjuf0NzM/PYGFhVqxDMaMEE4qqxjoU47KEYswY2nsGebWqmeuWFdvujD5Unp9B98AwTV02dNgvLKEYM4Zn9p1kcFh538o5sQ7FhFFekAlAbbMNHfYLSyjGjOE3u05QlpvOSpsd70sVpxOK9aP4hSUUY8Jo6x7g1apmbl5ZYs1dPlWak05SglBjQ4d9w9OEIiI3iMhBEakSkXvCvJ4qIo+6r1eKSHnIa/e65w+KyPUT3VNEfiwiNSKyw32s8vK9mZntmX0nGRpR3rfCmrv8KikxgXl5GVZD8ZEkr24sIonAA8B7gTrgdRHZrKr7QordAbSp6nkishH4BvBREVkGbASWA3OAZ0VksXvNePf8H6q6yav3ZM4dv3rzOPPzM7igdFasQzHjqCjIpMYSim94WUNZB1SparWqDgCPABtGldkAPOwebwKuFad9YQPwiKr2q2oNUOXebzL3NGZajrb08Fp1Cx9aU2bNXT63oNBJKMMjNnTYD7xMKKXAsZDnde65sGVUdQhoB/LHuXaie94nIrtE5H4RSQ0XlIjcKSLbRGRbU1PT1N+VmfE2bT+GCPzRRWWxDsVMYHFxNv1DIxy1RSJ9YSZ1yt8LLAUuBvKA/xWukKo+qKprVXVtYaHtvmfONDyibNpex+WLCpmTkx7rcMwElszOBuDgyc4YR2LA24RSD8wNeV7mngtbRkSSgADQMs61Y95TVU+oox/4EU7zmDFT8mpVM8fb+/jIWqudxIPzipwVDA41WELxAy8TyuvAIhGpEJEUnE72zaPKbAZuc48/BDyvzjoKm4GN7iiwCmARsHW8e4pIiftVgA8Aezx8b2aG+slrteRlptjeJ3EiIyWJeXkZllB8wrNRXqo6JCJ3A08DicAPVXWviHwV2Kaqm4GHgJ+KSBXQipMgcMs9BuwDhoC7VHUYINw93W/5cxEpBATYAXzKq/dmZqaa5m6eO9DIZ69ZRFpyYqzDMZO0uDjbEopPeJZQAFT1KeCpUee+HHLcB3x4jGvvA+6bzD3d89dMN15zbvvRqzUkJyTw8fXzYx2KmYIls7N44WAjA0MjpCTNpG7h+GOfvjFAc1c/j2+r45ZVcyjMDjtA0PjUktmzGBpRDjdaLSXWLKEYA3zvhbfoHxrmM1ctjHUoZopWlDprre2pb49xJMYSijnnnWzv46dbjvBHa8pYYPuexJ35eRlkpyWxq84SSqxZQjHnvH985iDDI8pfXrso1qGYs5CQIKwoDbDbaigxZwnFnNO2VLewaXsdf3HFAubmZcQ6HHOWVpQGOHCik4GhkViHck6zhGLOWX2Dw/zvX+2hLDedv7zGaifxbEVZgIHhERs+HGOWUMw5629/vY+qxi7u++AK0lNs3kk8u7AsB4A3jrbFOJJzmyUUc0762ZYj/GLrUT515UKuXGxrusW7stx0SgJpVNa0xjqUc5olFHPOeWJ7HX/z5B6uXVrEf79u8cQXGN8TEdZV5LG1phVn9SYTC5ZQzDljYGiEf3j6AH/1+E4uXZDPv35sDUmJ9l9gprikIp+mzn5qW2wp+1jxdOkVY/xAVXnhUBPf+O0BDpzs5CNry/jaB1bYMh0zzLqKPAC21rRQUZAZ42jOTZZQzIykquw/0clv95zgP3efoLqpm9KcdP79T9fy3mW2kvBMtLAwk8LsVF4+3MxHL54X63DOSZZQzIxy4GQHv955nP/cdYLalh4SxGkK+fSVC/nA6lKSrYlrxhIRrl5SyG/3nGRweMT+rWPAEoqJe32Dw/zHm/X8+NVaDjZ0kpggXLognzuvWMh1y4spyLLFHs8V1ywt5rFtdWyrbePShfmxDuecYwnFxC1V5Zdv1PN/f7uf5q4Bls+Zxd9tWM6NK0osiZyjLl9UQEpiAs/tb7CEEgOWUExcOtbaw5f+YzcvH27movm5fOfW1Vy6IB9nw05zrspMTeLShfn8ds9JvnTT+SQk2M9DNFkjo4krwyPKD16u5rr7X+KNI2383YblPP7JS3nXwgJLJgaAD64upf5UL1trbZJjtFkNxcSN/Sc6uOeJXeysa+eapUV87QMXMCcnPdZhGZ+5fvlsslKTeGJ7HesXWLNXNFkNxfhe3+Aw33rmIO//l1eoa+vlO7eu5qHb1loyMWGlpyRy04rZPLX7BO29g7EO55xiCcX4WmV1Czd/52X+5fkqblk1h2e/eCW3XDjHmrfMuP700nK6B4b52ZYjsQ7lnGJNXsaXWrr6+funDvDEG3WU5qTz8O3rbBFHM2kXlAa4cnEhP3q1htvfXWGrSUeJ1VCMr/QODPP9F9/imm+9yJM76vnMVQt59otXWjIxU3b3NefR3DXA9196K9ahnDOshmJ8obt/iMe3HeOBF96iqbOfKxYX8jc3n8+i4uxYh2bi1MXlebz/wjl894W3+MCqUsptfS/PWUIxMXW4oZOfVx7lie11dPYPsa4ijwc+tub0Qn/GTMf/vvl8XjjYyN2/eINNn3oXacnW9OUlSygm6o60dPObXSf49c7jHDjZSXKicNOKEj7xrnJWz8uNdXhmBimelcY/f3QVdzy8jS88uoNvb1xtq0x7yBKK8VxX/xCV1S28dKiJlw43U9PcDcCaeTn8n/cv4+aVJRRlp8U4SjNTXXt+MV9+3zK++pt9dD38Ovd/dJUtzeMRTxOKiNwAfBtIBH6gql8f9Xoq8BPgIqAF+Kiq1rqv3QvcAQwDf6mqT493TxGpAB4B8oHtwMdVdcDL92feqW9wmLeauthb38Gbx9p48+gpDjZ0ogppyQmsX5DPx9fP573LipmblxHrcM054vbLKshMTeRvntzLdfe/xKevXMitl8wjK9X+po4k8Wq7TBFJBA4B7wXqgNeBW1V1X0iZzwArVfVTIrIR+KCqflRElgG/ANYBc4BngeBerWHvKSKPAb9U1UdE5HvATlX9t/FiXLt2rW7bti2C73rmGhgaoat/iO7+Ibr6h2jrGaCho4+T7f00dPRxtLWHqsYujrX1EPyRmpWWxKp5uayem8PF5XmsLc+1NmwTUwdOdvC13+znlapmUpMSuHxRAWvm53J+ySzmBNKZPSuNrLQkEm0NsHGJyHZVXTv6vJfpeR1QparVbgCPABuAfSFlNgBfcY83Af8qzoy1DcAjqtoP1IhIlXs/wt1TRPYD1wAfc8s87N533IRytr7z3GE27zx+eu/q0ylZOfM5vKOMni6jZz4flddDE/2E1456nVGvn1lmjHjGeS8DQyMMDI8wluzUJEpz01lRFuC/rSnlvKIsls7OZkFBli3OZ3xl6exZ/OzPL+HNo208ueM4Lxxs5Nn9je8ol5KUQEZKImlJiSSIs9eKCCSM+ioQt5Ns//6DKyI++MXLhFIKHAt5XgdcMlYZVR0SkXacJqtSYMuoa0vd43D3zAdOqepQmPJnEJE7gTsB5s07u13dirJTWRIczipnfDn9wxX6IyYTlTn9uoQtf+a5UWVG3eSd3yvkHmOWOfM/xOjvlZKUQFZqIpmpSWS5j0B6MsWBNGbPSiPTmg1MnFk9L9cdALKc9p5BDjd2crKjj5PtfXT1D9E7MEzv4DB9g8OMqPOHl6rzp9yIqntO3/GHYDzJTI18a8E595tAVR8EHgSnyets7rFx3Tw2rrMtRo2ZCQIZyawtt2HqkeDl+Ll6YG7I8zL3XNgyIpIEBHA658e6dqzzLUCOe4+xvpcxxhgPeZlQXgcWiUiFiKQAG4HNo8psBm5zjz8EPK9OY/5mYKOIpLqjtxYBW8e6p3vNf7n3wL3nkx6+N2OMMaN41uTl9oncDTyNM8T3h6q6V0S+CmxT1c3AQ8BP3U73VpwEgVvuMZwO/CHgLlUdBgh3T/db/i/gERH5GvCme29jjDFR4tmw4Xhgw4aNMWbqxho2bGsQGGOMiQhLKMYYYyLCEooxxpiIsIRijDEmIs7pTnkRaQK82HS6AGj24L6RYvFNj8U3PRbf9Pghvvmq+o5tVM/phOIVEdkWbgSEX1h802PxTY/FNz1+js+avIwxxkSEJRRjjDERYQnFGw/GOoAJWHzTY/FNj8U3Pb6Nz/pQjDHGRITVUIwxxkSEJRRjjDERYQllGkRklYhsEZEdIrJNRNa550VEviMiVSKyS0TWhFxzm4gcdh+3jX33iMX4WRE5ICJ7ReSbIefvdeM7KCLXh5y/wT1XJSL3eB2f+z3/SkRURArc5774/ETkH9zPbpeI/IeI5IS85pvPzw/fOySGuSLyXyKyz/2Z+5x7Pk9Efu/+u/1eRHLd82P+W3scZ6KIvCkiv3GfV4hIpRvHo+72GLhbaDzqnq8UkfIoxJYjIpvcn739InKp3z6/MTnbWNrjbB7AM8CN7vFNwAshx7/F2WV3PVDpns8Dqt2vue5xrofxXQ08C6S6z4vcr8uAnUAqUAG8hbMdQKJ7vABIccss8/gznIuzHcERoMBnn991QJJ7/A3gG377/EJijdn3HhVHCbDGPc4GDrmf1zeBe9zz94R8lmH/raMQ5xeB/wf8xn3+GLDRPf4e8Gn3+DPA99zjjcCjUYjtYeDP3eMUIMdvn99YD6uhTI8Cs9zjAHDcPd4A/EQdW3B2kywBrgd+r6qtqtoG/B64wcP4Pg18XVX7AVS1MSS+R1S1X1VrgCpgnfuoUtVqVR0AHnHLeul+4H/ifJZBvvj8VPUZVR1yn27B2Qk0GJ9fPr+gWH7v01T1hKq+4R53AvuBUjeWh91iDwMfcI/H+rf2jIiUATcDP3CfC3ANsGmM+IJxbwKudct7FVsAuAJ3PydVHVDVU/jo8xuPJZTp+TzwDyJyDPhH4F73fClwLKRcnXturPNeWQxc7lbVXxSRi/0Un4hsAOpVdeeol3wR3yi34/wlyDhxxDK+WH7vsNzmodVAJVCsqifcl04Cxe5xLOL+Z5w/Ykbc5/nAqZA/HkJjOB2f+3q7W94rFUAT8CO3Se4HIpKJvz6/MXm2Y+NMISLPArPDvPTXwLXAF1T1CRH5CM5fFe/xUXxJOM1D64GLgcdEZEEUw5sovi/hNCvFzHjxqeqTbpm/xtk59OfRjC2eiUgW8ATweVXtCP2jXlVVRGIyX0FE3gc0qup2EbkqFjFMIAlYA3xWVStF5Ns4TVynxfLzm4gllAmo6pgJQkR+AnzOffo4bhUaqMfpGwgqc8/VA1eNOv+Ch/F9GvilOo2tW0VkBGdhubHiY5zzEY1PRFbg/DW20/1lUwa8Ic7ABl98fm6cnwDeB1zrfo6MEx/jnPfaeDFFlYgk4ySTn6vqL93TDSJSoqon3CaZYPNrtON+N3CLiNwEpOE0WX8bp6koya2FhMYQjK9ORJJwmrZbPIyvDqhT1Ur3+SachOKXz298sezAifcHTvvwVe7xtcB29/hmzuwo2+qezwNqcDqUc93jPA/j+xTwVfd4MU7VWIDlnNmpXI3TqZvkHlfwdsfu8ih9lrW83Snvl8/vBmAfUDjqvB8/v5h971FxCPAT4J9Hnf8HzuxU/uZ4/9ZRivUq3u6Uf5wzO+U/4x7fxZmd8o9FIa6XgSXu8Vfcz853n1/Y2GP5zeP9AVwGbHf/81YCF7nnBXgAZ9TNbmBtyDW343TiVgF/5nF8KcDPgD3AG8A1Ia/9tRvfQdyRau75m3BG5ryF0+wTrc8yNKH45fOrwknCO9zH9/z6+cX6e4fEcBnOAItdIZ/bTTj9Ds8Bh3FGHuZN9G8dhVhDE8oCYKv7b/44b4+MTHOfV7mvL4hCXKuAbe5n+CucP5589/mFe9jSK8YYYyLCRnkZY4yJCEsoxhhjIsISijHGmIiwhGKMMSYiLKEYY4yJCEsoxkSBiPy1u/ruLnFWp74k1jEZE2k2U94Yj4nIpTiz7deoar84y/SnTON+wRndxviK1VCM8V4J0Kxvr/rcrKrHReRiEfmDiOwUka0iki0iaSLyIxHZ7S4OeDU4S8CIyGYReR54TkQyReSH7nVvugttGhNTVkMxxnvPAF8WkUM4s5wfBV5zv35UVV8XkVlAL87acKqqK0RkKfCMiCx277MGWKmqrSLy98Dzqnq7OBt/bRWRZ1W1O9pvzpggq6EY4zFV7QIuAu7EWZr8UeCTwAlVfd0t0+E2Y12Gs1wOqnoAZ+OxYEL5vaq2usfXAfeIyA6cBTLTgHlReUPGjMFqKMZEgaoO4/zif0FEduMsOjhVobUPAf5IVQ9GIDxjIsJqKMZ4TESWiMiikFOrcFaqLglueub2nyThrDT7x+65xTi1jnBJ42ngs8HdA0VktYdvwZhJsRqKMd7LAv7F7esYwlm59k7gR+75dJz+k/cA3wX+za3FDAGfcEeGjb7n3+HsPLhLRBJwlvJ/XzTejDFjsdWGjTHGRIQ1eRljjIkISyjGGGMiwhKKMcaYiLCEYowxJiIsoRhjjIkISyjGGGMiwhKKMcaYiPj/6zX7mNScXl4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kGn1dFk2c5mV",
        "colab_type": "text"
      },
      "source": [
        "## Where to go from here?\n",
        "\n",
        "I am a bit frustrated by lack of stability that I am seeing in my implmentation of the Deep Q algorithm: sometimes the algorithm converges and sometimes not. Perhaps more tuning of hyper-parameters or use of a different optimization algorithm would exhibit better convergence. I have already spent more time than I had allocated on playing around with this agorithm so I am not going to try and fine-tune the hyperparamters or explore alternative optimization algorithms for now.\n",
        "\n",
        "Rather than spending time tuning hyperparameters I think it would be better use of my time to explore algorithmic improvements. In future posts I plan to cover the following extensions of the DQN algorithm: [Double Q-Learning](https://arxiv.org/abs/1509.06461), [Prioritized Experience Replay](https://arxiv.org/abs/1509.06461), and [Dueling Network Architectures](https://arxiv.org/abs/1511.06581)"
      ]
    }
  ]
}